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test_NMF.py
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test_NMF.py
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"""
Module for testing the NMF algorithm.
"""
import pandas as pd
import pytest
from surprise import Dataset, NMF, Reader
from surprise.model_selection import cross_validate
def test_NMF_parameters(u1_ml100k, pkf):
"""Ensure that all parameters are taken into account."""
# The baseline against which to compare.
algo = NMF(n_factors=1, n_epochs=1, random_state=1)
rmse_default = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
# n_factors
algo = NMF(n_factors=2, n_epochs=1, random_state=1)
rmse_factors = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_factors
# n_epochs
algo = NMF(n_factors=1, n_epochs=2, random_state=1)
rmse_n_epochs = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_n_epochs
# biased
algo = NMF(n_factors=1, n_epochs=1, biased=True, random_state=1)
rmse_biased = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_biased
# reg_pu
algo = NMF(n_factors=1, n_epochs=1, reg_pu=1, random_state=1)
rmse_reg_pu = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_reg_pu
# reg_qi
algo = NMF(n_factors=1, n_epochs=1, reg_qi=1, random_state=1)
rmse_reg_qi = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_reg_qi
# reg_bu
algo = NMF(n_factors=1, n_epochs=1, reg_bu=1, biased=True, random_state=1)
rmse_reg_bu = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_reg_bu
# reg_bi
algo = NMF(n_factors=1, n_epochs=1, reg_bi=1, biased=True, random_state=1)
rmse_reg_bi = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_reg_bi
# lr_bu
algo = NMF(n_factors=1, n_epochs=1, lr_bu=1, biased=True, random_state=1)
rmse_lr_bu = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_lr_bu
# lr_bi
algo = NMF(n_factors=1, n_epochs=1, lr_bi=1, biased=True, random_state=1)
rmse_lr_bi = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_lr_bi
# init_low
algo = NMF(n_factors=1, n_epochs=1, init_low=0.5, random_state=1)
rmse_init_low = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_init_low
# init_low
with pytest.raises(ValueError):
algo = NMF(n_factors=1, n_epochs=1, init_low=-1, random_state=1)
# init_high
algo = NMF(n_factors=1, n_epochs=1, init_high=0.5, random_state=1)
rmse_init_high = cross_validate(algo, u1_ml100k, ["rmse"], pkf)["test_rmse"]
assert rmse_default != rmse_init_high
def test_NMF_zero_ratings():
# Non-regression test for https://github.com/NicolasHug/Surprise/pull/367
reader = Reader(rating_scale=(-10, 10))
ratings_dict = {
"itemID": [0, 0, 0, 0, 1, 1],
"userID": [0, 1, 2, 3, 3, 4],
"rating": [-10, 10, 0, -5, 0, 5],
}
df = pd.DataFrame(ratings_dict)
data = Dataset.load_from_df(df[["userID", "itemID", "rating"]], reader)
trainset = data.build_full_trainset()
algo = NMF(n_factors=4, n_epochs=2)
algo.fit(trainset)